计算机科学 ›› 2018, Vol. 45 ›› Issue (7): 154-157.doi: 10.11896/j.issn.1002-137X.2018.07.026

• 信息安全 • 上一篇    下一篇

基于自适应卷积滤波的网络近邻入侵检测算法

卢强1,游荣义1,叶晓红2   

  1. 集美大学理学院 福建 厦门3610211 ;
    集美大学诚毅学院 福建 厦门3610212
  • 收稿日期:2017-07-21 出版日期:2018-07-30 发布日期:2018-07-30
  • 作者简介:卢 强(1981-),男,硕士,工程师,主要研究方向为计算机信息;游荣义(1957-),男,博士,教授,主要研究方向为信号处理、神经网络应用;叶晓红(1982-),女,硕士,工程师,主要研究方向为计算机通信,E-mail:cvss300/@sina.com.cn(通信作者)。
  • 基金资助:
    本文受福建省中青年教师教育科研项目:太阳能电池板光源自动跟随系统设计(JA15277)资助。

Network Nearest Neighbor Intrusion Detection Algorithm Based on Adaptive Convolution Filtering

LU Qiang1,YOU Rong-yi1,YE Xiao-hong2   

  1. School of Science,Jimei University,Xiamen,Fujian 361021,China1;
    Chengyi University College,Jimei University,Xiamen,Fujian 361021,China2
  • Received:2017-07-21 Online:2018-07-30 Published:2018-07-30

摘要: 深度无线传感组合网络中的近邻路由节点入侵具有载荷快速变化性,难以对新出现的攻击类型和网络异常行为进行有效识别,因此提出一种基于自适应卷积滤波的网络近邻入侵检测算法。在深度无线传感组合网络的传输信道中进行网络流量采集,构建网络入侵信号模型,在时间和频率上分析网络入侵信号的能量密度和攻击强度等特征信息,构建自适应卷积滤波器进行网络传输信息的盲源滤波和异常特征提取;采用联合时频分析方法进行网络近邻入侵特征信息的频谱参量估计,根据频谱特征的异常分布状态进行无线传感组合网络近邻入侵检测。仿真实验结果表明,采用该方法进行网络入侵检测的准确率较高,对未知的网络流量样本序列具有较高的识别能力和泛化能力,且所提算法优于传统的HHT检测算法、能量管理检测方法。

关键词: 检测, 卷积滤波, 入侵, 网络, 自适应

Abstract: The intrusion of the nearest neighbor routing nodes in the deep wireless sensor combination network has the characteristic of fast load variation,and it is difficult to effectively identify the types of attacks and abnormal network behavior.Therefore,this paper proposeda network nearest neighbor instrusion detection algorithm based on convolution filtering.Network traffic is collected in deep wireless sensor combination network,and network intrusion signal model is constructed.Energy density and attack strength of network intrusion signal are analyzed in terms of time and frequency,and blind source filtering and abnormal characteristic extraction of network information are achieved by constructing an adaptive convolution filter.Joint time-frequency analysis method is used to estimate the spectrum parameters of network intrusion feature neighbor information,and intrusion detection of wireless sensor network is done according to the abnormal distribution of spectrum features.Simulation results show that this method has high accuracy for network intrusion detection,has high recognition ability and generalization ability for the unknown network traffic sample sequence,and is superior to HHT detection method and energy management method.

Key words: Adaptive, Convolution filtering, Detection, Intrusion, Network

中图分类号: 

  • TP393.08
[1]LI H,QIAN C J,SUN L Z,et al.Simulation of a flexible polymer tethered to a flat adsorbing surface [J].Journal of Applied Polymer Science,2012,124(1):282-287.
[2]ZHAO X J,SUN Z X,YUAN Y.An Efficient Association Rule Mining Algorithm Based on Prejudging and Screening[J].Journal of Electronics & Information Technology,2016,38(7):1654-1659.(in Chinese)
赵学健,孙知信,袁源.基于预判筛选的高效关联规则挖掘算法[J].电子与信息学报,2016,38(7):1654-1659.
[3]WANG S,ZHAO B F.Network Intrusion Detection Based on Fuzzy Data Mining and Genetic Algorithm[J].Computer Measurement & Control,2012;20(3):660-663.(in Chinese)
王晟,赵壁芳.基于模糊数据挖掘和遗传算法的网络入侵检测技术[J].计算机测量与控制,2012,20(3):660-663.
[4]CECI M,MALERBA D.Classifying Web documents in a hierarchy of categories:a comprehensive study[J].Journal of Intelligent Information System,2007,28(1):37-78.
[5]ZHANG W M,CHEN Q Z.Network Intrusion Detection Algorithm Based on HHT with Shift Hierarchical Control[J].Computer Science,2014,41(12):107-111.(in Chinese)
章武媚,陈庆章.引入偏移量递阶控制的网络入侵HHT检测算法[J].计算机科学,2014,41(12):107-111.
[6]CHEN H,WAN G X,XIAO Z J.Intrusion detection method of deep belief network model based on optimization of data processing[J].Journal of Computer Applications,2017,37(6):1636-1643.(in Chinese)
陈虹,万广雪,肖振久.基于优化数据处理的深度信念网络模型的入侵检测方法[J].计算机应用,2017,37(6):1636-1643.
[7]LI F,WU C M.Research on Prevention Fluctuation Control method of Network Intrusion Based on Energy Management[J].Computer Simulation,2013,30(12):45-48.(in Chinese)
黎峰,吴春明.基于能量管理的网络入侵防波动控制方法研究[J].计算机仿真,2013,30(12):45-48.
[8]DENG Z H,CAO L B,JIANG Y Z,et al.Minimax probability TSK fuzzy system classifier:A more transparent and highly interpretable classification model[J].IEEE Transactions on Fuzzy Systems,2015,23(4):813-826.
[9]HESS R A.Aircraft and rotorcraft system identification-engineering methods with flight test examples[J].Journal of Gui-dance,Control,and Dynamics,2013,36(4):1249-1250.
[10]ZHANG H B,HE Q B,KONG F R.Stochastic resonance in an underdamped system with pinning potential for weak signal detection[J].Sensors,2015,15(9):21169-21195.
[11]WANG H X,WANG S Y,WANG X,et al.Analysis of LFM signals and improvement of IFM system[J].Acta Armamentarii,2014,35(8):1193-1199.(in Chinese)
王洪迅,王士岩,王星,等.瞬时测频系统的线性调频信号分析及改进[J].兵工学报,2014,35(8):1193-1199.
[12]MAHBOUBI H,MOEZZI K,AGHDAM A G,et al.Distributed deployment algorithms for improved coverage in a network of wireless mobile sensors[J].IEEE Transactions on Industrial Informatics,2014,10(1):163-174.
[13]MAHBOUBI H.Distributed deployment algorithms for efficient coverage in a network of mobile sensors with nonidentical sen-sing Capabilities[J].IEEE Transactions on Vehicular Technology,2014,63(8):3998-4016.
[14]DAI W.Application of Intrusion Detection Technology in Network Security.Journal of Chongqing University of Technology(Natural Science),2018,32(4):156-160,185.(in Chinese)
代威.入侵检测技术在网络安全中的应用.重庆理工大学学报(自然科学),2018,32(4):156-160,185.
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